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Context Aware Contrastive Opinion Summarization

Abstract : Model-based approaches for context-sensitive contrastive summarization depend on hand-crafted features for producing a summary. Deriving these hand-crafted features using machine learning algorithms is computationally expensive. This paper presents a deep learning approach to provide an end-to-end solution for context-sensitive contrastive summarization. A hierarchical attention model referred to as Contextual Sentiment LSTM (CSLSTM) is proposed to automatically learn the representations of context, feature and opinion words present in review documents of each entity. The resultant document context vector is a high-level representation of the document. It is used as a feature for context-sensitive classification and summarization. Given a set of summaries from positive class and a negative class of two entities, the summaries which have high contrastive score are identified and presented as context-sensitive contrastive summaries. Experimental results on restaurant dataset show that the proposed model achieves better performance than the baseline models.
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https://hal.inria.fr/hal-03434805
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Submitted on : Thursday, November 18, 2021 - 2:21:42 PM
Last modification on : Thursday, November 18, 2021 - 2:24:35 PM
Long-term archiving on: : Saturday, February 19, 2022 - 7:14:42 PM

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Distributed under a Creative Commons Attribution 4.0 International License

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S. K. Lavanya, B. Parvathavarthini. Context Aware Contrastive Opinion Summarization. 3rd International Conference on Computational Intelligence in Data Science (ICCIDS), Feb 2020, Chennai, India. pp.16-29, ⟨10.1007/978-3-030-63467-4_2⟩. ⟨hal-03434805⟩

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